This course will introduce you to the concepts and methods of causal inference and causal modeling in the social sciences. It will highlight the relevance of research design, analytical methods, and their systematic combination to optimize the validity of causal inferences drawn from empirical studies. You will learn the key principles and techniques of causal inference, including potential outcomes, counterfactuals, and causal graphs, and will get to know the experimental approach to causality. Building on existing knowledge concerning linear regression modelling and research design, the course will then cover key methods of causal modeling using survey data, such as fixed effects panel models, matching, difference-in-differences, regression discontinuity, and instrumental variables. Throughout the course, you will apply these concepts and methods in hands-on sessions to real-world examples in the social sciences. The application will be conducted with the statistical software package Stata. A solid background in Stata is expected. By the end of the course, you will have the skills and knowledge to design, conduct, and interpret causal inference studies in the social sciences. You will be able to engage with the contemporary literature of causal inference and identify state-of-the-art methods which might be most relevant to your specific research question.

 

The full syllabus of the course including the day-to-day schedule will be published here in April.

You will find the course useful if you:

By the end of the course you will:

The course will be split into three-hour morning and three-hour afternoon sessions, including coffee breaks. In order to secure a close link between the learning and the application of contents, we will switch between lecture format (~50%) and hands-on exercises, tutorials, or lab sessions (~50%) in a flexible way. In addition to shorter exercises, a selected number of more in-depth assignments will be provided which participants solve in groups of 2-3. These include the application of causal inference methods to estimate effects based on existing datasets using Stata. Lecturers will be available for individual consultations to support work on group assignments and to facilitate discussions within groups.

You will need to bring a laptop computer with a recent version of Stata (13 or higher) installed to successfully participate in this course.

 

GESIS will provide you with short term licenses for Stata for the duration of the course if needed.